RASCL: Rapid Assessment of Selection in CLades through molecular sequence analysis.

An important unmet need revealed by the COVID-19 pandemic is the near-real-time identification of potentially fitness-altering mutations within rapidly growing SARS-CoV-2 lineages. Although powerful molecular sequence analysis methods are available to detect and characterize patterns of natural selection within modestly sized gene-sequence datasets, the computational complexity of these methods and their sensitivity to sequencing errors render them effectively inapplicable in large-scale genomic surveillance contexts. Motivated by the need to analyze new lineage evolution in near-real time using large numbers of genomes, we developed the Rapid Assessment of Selection within CLades (RASCL) pipeline. RASCL applies state of the art phylogenetic comparative methods to evaluate selective processes acting at individual codon sites and across whole genes. RASCL is scalable and produces automatically updated regular lineage-specific selection analysis reports: even for lineages that include tens or hundreds of thousands of sampled genome sequences. Key to this performance is (i) generation of automatically subsampled high quality datasets of gene/ORF sequences drawn from a selected "query" viral lineage; (ii) contextualization of these query sequences in codon alignments that include high-quality "background" sequences representative of global SARS-CoV-2 diversity; and (iii) the extensive parallelization of a suite of computationally intensive selection analysis tests. Within hours of being deployed to analyze a novel rapidly growing lineage of interest, RASCL will begin yielding JavaScript Object Notation (JSON)-formatted reports that can be either imported into third-party analysis software or explored in standard web-browsers using the premade RASCL interactive data visualization dashboard. By enabling the rapid detection of genome sites evolving under different selective regimes, RASCL is well-suited for near-real-time monitoring of the population-level selective processes that will likely underlie the emergence of future variants of concern in measurably evolving pathogens with extensive genomic surveillance.

Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes.

Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type-the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity-bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.

Assessment of BCOR Internal Tandem Duplications in Pediatric Cancers by Targeted RNA Sequencing.

Alterations in the BCOR gene, including internal tandem duplications (ITDs) of exon 15 have emerged as important oncogenic changes that define several diagnostic entities. In pediatric cancers, BCOR ITDs have recurrently been described in clear cell sarcoma of kidney (CCSK), primitive myxoid mesenchymal tumor of infancy (PMMTI), and central nervous system high-grade neuroepithelial tumor with BCOR ITD in exon 15 (HGNET-BCOR ITDex15). In adults, BCOR ITDs are also reported in endometrial and other sarcomas. The utility of multiplex targeted RNA sequencing for the identification of BCOR ITD in pediatric cancers was investigated. All available archival cases of CCSK, PMMTI, and HGNET-BCOR ITDex15 were collected. Each case underwent anchored multiplex PCR library preparation with a custom-designed panel, with BCOR targeted for both fusions and ITDs. BCOR ITD was detected in all cases across three histologic subtypes using the RNA panel, with no other fusions identified in any of the cases. All BCOR ITDs occurred in the final exon, within 16 codons from the stop sequence. Multiplex targeted RNA sequencing from formalin-fixed, paraffin-embedded tissue is successful at identifying BCOR internal tandem duplications. This analysis supports the use of anchored multiplex PCR targeted RNA next-generation sequencing panels for identification of BCOR ITDs in pediatric tumors. The use of post-analytic algorithms to improve the detection of BCOR ITD using DNA panels was also explored.

Mathematical model of a short translatable G-quadruplex and an assessment of its relevance to misfolding-induced proteostasis.

G-quadruplexes can form in protein coding and non-coding segments such as the untranslated regions and introns of the mRNA transcript of several genes. This implies that amino acid forms of the G-quadruplex may have important consequences for protein homeostasis and the diseases caused by their alterations thereof. However, the absence of a suitable model and multitude of predicted physical forms has precluded a comprehensive enumeration and analysis of potential translatable G-quadruplexes. In this manuscript a mathematical model of a short translatable G-quadruplex (TG4) in the protein coding segment of the mRNA of a hypothetical gene is presented. Several novel indices (α, ß) are formulated and utilized to categorize and select codons along with the amino acids that they code for. A generic algorithm is then iteratively deployed which computes the entire complement of peptide members that TG4 corresponds to, i.e., PTG4~TG4. The presence, distribution and relevance of this peptidome to protein sequence is investigated by comparing it with disorder promoting short linear motifs. In frame termination codon, co-occurrence, homology and distribution of overlapping/shared amino acids suggests that TG4 (~PTG4) may facilitate misfolding-induced proteostasis. The findings presented rigorously argue for the existence of a unique and potentially clinically relevant peptidome of a short translatable G-quadruplex that could be used as a diagnostic- or prognostic-screen of certain proteopathies.

Clinical assessment of the GNAS mutation status in patients with intraductal papillary mucinous neoplasm of the pancreas.

Intraductal papillary mucinous neoplasm (IPMN) of the pancreas is characterized by cystic dilation of the pancreatic duct, caused by mucin hypersecretion, with slow progression via the adenoma-carcinoma sequence mechanism. Mutation of GNAS at codon 201 is found exclusively in IPMNs, occurring at a rate of 41-75%. Recent advances in molecular biological techniques have demonstrated that GNAS mutation might play a role in the transformation of IPMNs after the appearance of neoplastic cells, rather than in the tumorigenesis of IPMNs. GNAS mutation is observed frequently in the intestinal subtype of IPMNs with MUC2 expression, and less frequently in IPMNs with concomitant pancreatic ductal adenocarcinoma (PDAC). Research has focused on assessing GNAS mutation status in clinical practice using various samples. In this review, we discuss the clinical application of GNAS mutation assessment to differentiate invasive IPMNs from concomitant PDAC, examine the clonality of recurrent IPMNs in the remnant pancreas using resected specimens, and differentiate pancreatic cystic lesions using cystic fluid collected by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), duodenal fluid, and serum liquid biopsy samples.

TP53 and DNA-repair gene polymorphisms genotyping as a low-cost lung adenocarcinoma screening tool.

AIM: TP53 and DNA repair polymorphisms have been proposed as cancer risk factors. This study evaluated the usability of TP53 Arg72Pro single-nucleotide polymorphism, X RCC1 Arg399Gln and RAD51 G135C as a low-cost lung adenocarcinoma screening tool. PATIENTS AND METHODS: This case-control study included 78 atients with lung adenocarcinoma and 79 healthy matched controls. TP53, XRCC1 and RAD51 genotyping was done by PCR followed by restriction length polymorphism. Descriptive analyses included genotype and allelic frequencies and deviations of the frequencies from those expected under Hardy-Weinberg equilibrium were assessed using the χ2 test. The OR and 95% CIs were calculated as an estimate of relative risk, with significance set at p value <0.05. RESULTS: The TP53 codon 72 Pro allele and the XRCC1 codon 399 Arg allele in a homozygous state were associated with lung adenocarcinoma (p=0.037; OR (95% CI) 2.42 (1.10 to 5.31)), that is, p=0.037; OR (95% CI) 2.16 (1.08 to 4.33), respectively. Also, carriers of the TP53 codon 72 Pro allele and the XRCC1 codon 399 ArgArg genotype older than 50 showed an even higher risk of developing lung adenocarcinoma (p=0.03 in both cases). CONCLUSIONS: The TP53 codon 72 Arg allele and XRCC1 codon 399 Gln allele are likely to have a protective effect against lung adenocarcinoma, especially in individuals older than 50 years of age. XRCC1 and TP53 genotyping might be a useful low-cost tool for evaluating individual lung cancer risk, leading to earlier detection and management of this disease.

Decomposition of time-dependent fluorescence signals reveals codon-specific kinetics of protein synthesis.

During protein synthesis, the nascent peptide chain traverses the peptide exit tunnel of the ribosome. We monitor the co-translational movement of the nascent peptide using a fluorescent probe attached to the N-terminus of the nascent chain. Due to fluorophore quenching, the time-dependent fluorescence signal emitted by an individual peptide is determined by co-translational events, such as secondary structure formation and peptide-tunnel interactions. To obtain information on these individual events, the measured ensemble fluorescence signal has to be decomposed into position-dependent intensities. Here, we describe mRNA translation as a Markov process with specific fluorescence intensities assigned to the different states of the process. Combining the computed stochastic time evolution of the translation process with a sequence of observed ensemble fluorescence time courses, we compute the unknown position-specific intensities and obtain detailed information on the kinetics of the translation process. In particular, we find that translation of poly(U) mRNAs dramatically slows down at the fourth UUU codon. The method presented here detects subtle differences in the position-specific fluorescence intensities and thus provides a novel approach to study translation kinetics in ensemble experiments.

Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources.

Recent studies have demonstrated how the competition for the finite pool of available gene expression factors has important effect on fundamental gene expression aspects. In this study, based on a whole-cell model simulation of translation in S. cerevisiae, we evaluate for the first time the expected effect of mRNA levels fluctuations on translation due to the finite pool of ribosomes. We show that fluctuations of a single gene or a group of genes mRNA levels induce periodic behavior in all S. cerevisiae translation factors and aspects: the ribosomal densities and the translation rates of all S. cerevisiae mRNAs oscillate. We numerically measure the oscillation amplitudes demonstrating that fluctuations of endogenous and heterologous genes can cause a significant fluctuation of up to 50% in the steady-state translation rates of the rest of the genes. Furthermore, we demonstrate by synonymous mutations that oscillating the levels of mRNAs that experience high ribosomal occupancy (e.g. ribosomal "traffic jam") induces the largest impact on the translation of the S. cerevisiae genome. The results reported here should provide novel insights and principles related to the design of synthetic gene expression circuits and related to the evolutionary constraints shaping gene expression of endogenous genes.

Molecular cloning, codon-optimized gene expression, and bioactivity assessment of two novel fungal immunomodulatory proteins from Ganoderma applanatum in Pichia.

Fungal immunomodulatory proteins (FIPs) have been identified from a series of fungi, especially in Ganoderma species. However, little is known about the FIPs from G. applanatum. In this study, two novel FIP genes, termed as FIP-gap1 and FIP-gap2, were cloned from G. applanatum, characterized and functionally expressed after codon optimization in Pichia pastoris GS115. Results showed that FIP-gap1 and FIP-gap2 comprised 342-bp encoding peptides of 113 amino acids, which shared a high homology with other Ganoderma FIPs. The yield of recombinant FIP-gap1 and FIP-gap2 increased significantly after codon optimization and reached 247.4 and 197.5 mg/L, respectively. Bioactivity assay in vitro revealed that both rFIP-gap1 and rFIP-gap2 could agglutinate mouse, sheep, and human red blood cells. Besides, rFIP-gap1 and rFIP-gap2 obviously stimulated the proliferation of mouse splenocytes and enhanced IL-2 and IFN-γ release. Cytotoxicity detection indicated that IC50 of rFIP-gap1 towards A549 and HeLa cancer cells were 29.89 and 8.34 µg/mL, respectively, whereas IC50 of rFIP-gap2 to the same cancer cells were 60.92 and 41.05 µg/mL, respectively. Taken together, novel FIP gaps were cloned and functionally expressed in P. pastoris, which can serve as feasible and stable resources of rFIP gaps for further studies and potential applications.

Phylogenomic relationship of feijoa (Acca sellowiana (O.Berg) Burret) with other Myrtaceae based on complete chloroplast genome sequences.

Given their distribution, importance, and richness, Myrtaceae species comprise a model system for studying the evolution of tropical plant diversity. In addition, chloroplast (cp) genome sequencing is an efficient tool for phylogenetic relationship studies. Feijoa [Acca sellowiana (O. Berg) Burret; CN: pineapple-guava] is a Myrtaceae species that occurs naturally in southern Brazil and northern Uruguay. Feijoa is known for its exquisite perfume and flavorful fruits, pharmacological properties, ornamental value and increasing economic relevance. In the present work, we reported the complete cp genome of feijoa. The feijoa cp genome is a circular molecule of 159,370 bp with a quadripartite structure containing two single copy regions, a Large Single Copy region (LSC 88,028 bp) and a Small Single Copy region (SSC 18,598 bp) separated by Inverted Repeat regions (IRs 26,372 bp). The genome structure, gene order, GC content and codon usage are similar to those of typical angiosperm cp genomes. When compared to other cp genome sequences of Myrtaceae, feijoa showed closest relationship with pitanga (Eugenia uniflora L.). Furthermore, a comparison of pitanga synonymous (Ks) and nonsynonymous (Ka) substitution rates revealed extremely low values. Maximum Likelihood and Bayesian Inference analyses produced phylogenomic trees identical in topology. These trees supported monophyly of three Myrtoideae clades.

Assessment of BRAF V600E (VE1) protein expression and BRAF gene mutation status in codon 600 in benign and malignant salivary gland neoplasms.

BACKGROUND: The role of BRAF mutations in cancerogenesis has been demonstrated in several solid tumor types. However, in salivary gland tumors, this genetic alteration is very uncommon, and its role still remains unclear. Thus, the aim of this study was to analyze BRAF V600E (VE1) protein expression with BRAF mutation status in codon 600, in malignant and benign salivary gland tumors. METHODS: Studies were performed on archived formalin-fixed paraffin-embedded tissue sections derived from 95 patients who underwent surgery for tumors of the salivary gland. Immunohistochemical staining (IHC) on tissue microarray slides was performed for evaluation of BRAF V600E (VE1) protein expression, and the automatic molecular diagnostics platform was used for the evaluation of mutations in codon 600 of BRAF gene. RESULTS: IHC cytoplasmic expression of BRAF V600E (VE1) protein was found in two of 95 cases: one case of adenocarcinoma NOS (one of three; 33%) and one case of carcinoma ex pleomorphic adenoma (one of five; 20%). Although, in IHC studies, nuclear BRAF V600E (VE1) protein expression was found in 14 (15%) of the analyzed cases: nine of 28 (32%) cases of pleomorphic adenoma, three of five (60%) cases of ductal carcinoma, one of nine (11%) case of mucoepidermoid carcinoma, and in one of five (20%) case of carcinoma ex pleomorphic adenoma. All cases were negative for polymerase chain reaction PCR-based analyses of BRAF mutations in codon 600. CONCLUSIONS: In studied salivary gland cancers, no PCR-based prove mutations of BRAF V600 were detected. Further molecular analyses are necessary to rapid molecular arrays for the identification of specific mutations, optimal for individualized targeted therapies.

Smoothed Bootstrap Aggregation for Assessing Selection Pressure at Amino Acid Sites.

To detect positive selection at individual amino acid sites, most methods use an empirical Bayes approach. After parameters of a Markov process of codon evolution are estimated via maximum likelihood, they are passed to Bayes formula to compute the posterior probability that a site evolved under positive selection. A difficulty with this approach is that parameter estimates with large errors can negatively impact Bayesian classification. By assigning priors to some parameters, Bayes Empirical Bayes (BEB) mitigates this problem. However, as implemented, it imposes uniform priors, which causes it to be overly conservative in some cases. When standard regularity conditions are not met and parameter estimates are unstable, inference, even under BEB, can be negatively impacted. We present an alternative to BEB called smoothed bootstrap aggregation (SBA), which bootstraps site patterns from an alignment of protein coding DNA sequences to accommodate the uncertainty in the parameter estimates. We show that deriving the correction for parameter uncertainty from the data in hand, in combination with kernel smoothing techniques, improves site specific inference of positive selection. We compare BEB to SBA by simulation and real data analysis. Simulation results show that SBA balances accuracy and power at least as well as BEB, and when parameter estimates are unstable, the performance gap between BEB and SBA can widen in favor of SBA. SBA is applicable to a wide variety of other inference problems in molecular evolution.

A Mutation Model from First Principles of the Genetic Code.

The paper presents a neutral Codons Probability Mutations (CPM) model of molecular evolution and genetic decay of an organism. The CPM model uses a Markov process with a 20-dimensional state space of probability distributions over amino acids. The transition matrix of the Markov process includes the mutation rate and those single point mutations compatible with the genetic code. This is an alternative to the standard Point Accepted Mutation (PAM) and BLOcks of amino acid SUbstitution Matrix (BLOSUM). Genetic decay is quantified as a similarity between the amino acid distribution of proteins from a (group of) species on one hand, and the equilibrium distribution of the Markov chain on the other. Amino acid data for the eukaryote, bacterium, and archaea families are used to illustrate how both the CPM and PAM models predict their genetic decay towards the equilibrium value of 1. A family of bacteria is studied in more detail. It is found that warm environment organisms on average have a higher degree of genetic decay compared to those species that live in cold environments. The paper addresses a new codon-based approach to quantify genetic decay due to single point mutations compatible with the genetic code. The present work may be seen as a first approach to use codon-based Markov models to study how genetic entropy increases with time in an effectively neutral biological regime. Various extensions of the model are also discussed.

Modeling the effect of codon translation rates on co-translational protein folding mechanisms of arbitrary complexity.

In a cell, the folding of a protein molecule into tertiary structure can begin while it is synthesized by the ribosome. The rate at which individual amino acids are incorporated into the elongating nascent chain has been shown to affect the likelihood that proteins will populate their folded state, indicating that co-translational protein folding is a far from equilibrium process. Developing a theoretical framework to accurately describe this process is, therefore, crucial for advancing our understanding of how proteins acquire their functional conformation in living cells. Current state-of-the-art computational approaches, such as molecular dynamics simulations, are very demanding in terms of the required computer resources, making the simulation of co-translational protein folding difficult. Here, we overcome this limitation by introducing an efficient approach that predicts the effects that variable codon translation rates have on co-translational folding pathways. Our approach is based on Markov chains. By using as an input a relatively small number of molecular dynamics simulations, it allows for the computation of the probability that a nascent protein is in any state as a function of the translation rate of individual codons along a mRNA's open reading frame. Due to its computational efficiency and favorable scalability with the complexity of the folding mechanism, this approach could enable proteome-wide computational studies of the influence of translation dynamics on co-translational folding.

Representation of DNA sequences in genetic codon context with applications in exon and intron prediction.

To apply digital signal processing (DSP) methods to analyze DNA sequences, the sequences first must be specially mapped into numerical sequences. Thus, effective numerical mappings of DNA sequences play key roles in the effectiveness of DSP-based methods such as exon prediction. Despite numerous mappings of symbolic DNA sequences to numerical series, the existing mapping methods do not include the genetic coding features of DNA sequences. We present a novel numerical representation of DNA sequences using genetic codon context (GCC) in which the numerical values are optimized by simulation annealing to maximize the 3-periodicity signal to noise ratio (SNR). The optimized GCC representation is then applied in exon and intron prediction by Short-Time Fourier Transform (STFT) approach. The results show the GCC method enhances the SNR values of exon sequences and thus increases the accuracy of predicting protein coding regions in genomes compared with the commonly used 4D binary representation. In addition, this study offers a novel way to reveal specific features of DNA sequences by optimizing numerical mappings of symbolic DNA sequences.

A generalized mechanistic codon model.

Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 × 61 codon substitution rate matrix is created using Kronecker product of three 4 × 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.

Rate-limiting steps in yeast protein translation.

Deep sequencing now provides detailed snapshots of ribosome occupancy on mRNAs. We leverage these data to parameterize a computational model of translation, keeping track of every ribosome, tRNA, and mRNA molecule in a yeast cell. We determine the parameter regimes in which fast initiation or high codon bias in a transgene increases protein yield and infer the initiation rates of endogenous Saccharomyces cerevisiae genes, which vary by several orders of magnitude and correlate with 5' mRNA folding energies. Our model recapitulates the previously reported 5'-to-3' ramp of decreasing ribosome densities, although our analysis shows that this ramp is caused by rapid initiation of short genes rather than slow codons at the start of transcripts. We conclude that protein production in healthy yeast cells is typically limited by the availability of free ribosomes, whereas protein production under periods of stress can sometimes be rescued by reducing initiation or elongation rates.

Mutant KRAS codon 12 and 13 alleles in patients with metastatic colorectal cancer: assessment as prognostic and predictive biomarkers of response to panitumumab.

PURPOSE: Panitumumab, a fully human monoclonal antibody targeting the epidermal growth factor receptor (EGFR), has demonstrated significant improvements in progression-free survival (PFS) in patients with wild-type KRAS metastatic colorectal cancer (mCRC) in studies 20050203 (first line), 20050181 (second line), and 20020408 (monotherapy). Mutations in KRAS codons 12 and 13 are recognized biomarkers that predict lack of response to anti-EGFR antibody therapies. This retrospective analysis of three randomized phase III studies assessed the prognostic and predictive impact of individual mutant KRAS codon 12 and 13 alleles. PATIENTS AND METHODS: Patients were randomly assigned 1:1 to FOLFOX4 (infusional fluorouracil, leucovorin, and oxaliplatin) in study 20050203, FOLFIRI (fluorouracil, leucovorin, and irinotecan) in study 20050181, or best supportive care in study 20020408 with or without panitumumab 6.0 mg/kg once every 2 weeks. In all, 441 (20050203), 486 (20050181), and 126 (20020408) patients with mutant KRAS codon 12 or 13 alleles were included in the analysis. RESULTS: No mutant KRAS allele in patients treated on the control arm emerged as a consistent prognostic factor for PFS or overall survival (OS). In addition, no mutant KRAS allele was consistently identified as a predictive factor for PFS or OS in patients receiving panitumumab treatment. Significant interactions for individual mutant KRAS alleles were observed only in study 20050203 with G13D negatively and G12V positively associated with OS in the panitumumab-containing arm. Pooled analysis indicated that only G12A was associated with a negative predictive effect on OS. CONCLUSION: In this retrospective analysis, results across three treatment regimens suggest that patients with mutant KRAS codon 12 or 13 mCRC tumors are unlikely to benefit from panitumumab therapy. Currently, panitumumab therapy should be limited to patients with wild-type KRAS mCRC.

Estimating empirical codon hidden Markov models.

Empirical codon models (ECMs) estimated from a large number of globular protein families outperformed mechanistic codon models in their description of the general process of protein evolution. Among other factors, ECMs implicitly model the influence of amino acid properties and multiple nucleotide substitutions (MNS). However, the estimation of ECMs requires large quantities of data, and until recently, only few suitable data sets were available. Here, we take advantage of several new Drosophila species genomes to estimate codon models from genome-wide data. The availability of large numbers of genomes over varying phylogenetic depths in the Drosophila genus allows us to explore various divergence levels. In consequence, we can use these data to determine the appropriate level of divergence for the estimation of ECMs, avoiding overestimation of MNS rates caused by saturation. To account for variation in evolutionary rates along the genome, we develop new empirical codon hidden Markov models (ecHMMs). These models significantly outperform previous ones with respect to maximum likelihood values, suggesting that they provide a better fit to the evolutionary process. Using ECMs and ecHMMs derived from genome-wide data sets, we devise new likelihood ratio tests (LRTs) of positive selection. We found classical LRTs very sensitive to the presence of MNSs, showing high false-positive rates, especially with small phylogenies. The new LRTs are more conservative than the classical ones, having acceptable false-positive rates and reduced power.